This document analyzes Medicare cost data using unsupervised and supervised machine learning techniques. Clustering identified three clusters of medical procedures based on attributes like cost, patient age and provider type. Decision trees and logistic regression found consistency between state and national average costs, and predictive models estimated procedure costs based on state, number of services and providers. Recommendations included increasing physician enrollment in under-represented states and instituting stricter rules for commonly abused high-cost procedures.